We develop computational mechanical modeling and methods for the analysis and simulation of the motions of a human body. This type of work is crucial in many aspects of human life, ranging from comfort in riding, the motion of aged persons, sports performance and injuries, and many ergonomic issues. A prevailing approach for human motion studies is through lumped parameter models containing discrete masses for the parts of the human body with empirically determined spring, mass, damping coefficients. Such models have been effective to some extent; however, a much more faithful modeling method is to model the human body as it is, namely, as a continuum. We present this approach, and for comparison, we choose two digital CAD models of mannequins for a standing human body, one from the versatile software package LS-DYNA and another from open resources with some of our own adaptations. Our basic view in this paper is to regard human motion as a perturbation and vibration from an equilibrium position which is upright standing. A linear elastodynamic model is chosen for modal analysis, but a full nonlinear viscoelastoplastic extension is possible for full-body simulation. The motion and vibration of these two mannequin models is analyzed by modal analysis, where the normal vibration modes are determined. LS-DYNA is used as the supercomputing and simulation platform. Four sets of low-frequency modes are tabulated, discussed, visualized, and compared. Higher frequency modes are also selectively displayed. We have found that these modes of motion and vibration form intrinsic basic modes of biomechanical motion of the human body. This view is supported by our finding of the upright walking motion as a low-frequency mode in modal analysis. Such a "walking mode" shows the in-phase and out-of-phase movements between the legs and arms on the left and right sides of a human body, implying that this walking motion is spontaneous, likely not requiring any directives from the brain. Dynamic motions of CAD mannequins are also simulated by drop tests for comparisons and the validity of the models is discussed through Fourier frequency analysis. All computed modes of motion are collected in several sets of video animations for ease of visualization. Samples of LS-DYNA computer codes are also included for possible use by other researchers.
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http://dx.doi.org/10.1002/cnm.3841 | DOI Listing |
Amino Acids
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Department of Nutrition, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
Recent studies have suggested that the interaction between diet and an individual's genetic predisposition can determine the likelihood of obesity and various metabolic disorders. The current study aimed to examine the association of dietary branched-chain amino acids(BCAAs) and aromatic amino acids(AAAs) with the expression of the leptin and FTO genes in the visceral and subcutaneous adipose tissues of individuals undergoing surgery. This cross-sectional study was conducted on 136 Iranian adults, both men and women, aged ≥18 years.
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Pediatric Cell, and Gene Therapy Research Center Gene, Cell and Tissue Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
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View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Biomedical Engineering, Bahçeşehir University, Çırağan Caddesi Osmanpaşa Mektebi Sokak No: 4-6 Beşiktaş, İstanbul, 34353, Turkey.
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First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical, University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Institute of Nephrology, Jinan, China.
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View Article and Find Full Text PDFJ Phys Chem Lett
January 2025
Department of Physics, Rutgers University, Newark, New Jersey 07102, United States of America.
Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments.
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